Prevalence of and risk factors for leptospirosis among dogs in the United States and Canada: 677 cases (1970–1998)
Bibliographic record
Abstract
OBJECTIVE: To determine whether there was a temporal trend in prevalence of leptospirosis among dogs in the United States and Canada and to determine whether age, sex, and breed were risk factors for the disease. DESIGN: Retrospective study. ANIMALS: 1,819,792 dogs examined at 22 veterinary teaching hospitals between 1970 and 1998. PROCEDURES: The Veterinary Medical Data Base was searched for records of dogs in which a diagnosis of leptospirosis was made, and hospital prevalence was calculated. Logistic regression was used to examine the association between leptospirosis and age, sex, and breed. RESULTS: 677 dogs with leptospirosis were identified. Thus, hospital prevalence was 37 cases/100,000 dogs examined. A significant increase in leptospirosis prevalence between 1983 and 1998 was identified. Male dogs were at significantly greater risk of leptospirosis than were female dogs; dogs between 4 and 6.9 years old and between 7 and 10 years old were at significantly greater risk than dogs < 1 year old; and herding dogs, hounds, working dogs, and mixed-breed dogs were at significantly greater risk than companion dogs. CONCLUSIONS AND CLINICAL RELEVANCE: The prevalence of leptospirosis among dogs examined at veterinary teaching hospitals in the United States and Canada has increased significantly since 1983. Male dogs of working and herding breeds were at greater risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".